System and method for thought object selection

ABSTRACT

Systems and methods for processing qualitative responses from a plurality of user devices whereby a selection of a next thought object, to deliver to a first user device, may be based on a plurality of qualitative responses received from a plurality of user devices. In a preferred embodiment, a thought object selection computer may compute the selection by determining a filtered set of thought objects by custom selection. In some embodiments, if the quantity of the filtered set of thought objects is greater than a pre-configured amount the selection may be computed by randomly selecting a subset of the filtered set of thought objects. Further filtering the filtered set of thought objects by determining one or more least seen thought objects, and selecting a most diverse thought object, updating the filtered set of thought objects and sending the filtered set of thought objects to the first user device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 15/066,303, titled “PROCESSING QUALITATIVE RESPONSES”, whichwas filed on March 10, which is a continuation of International PatentApplication No. PCT/CA2014/050889, filed Sep. 17, 2014, which claimspriority to each of U.S. Provisional Application No. 61/880,578, filedSep. 20, 2013, and U.S. Provisional Application No. 61/951,044, filedMar. 11, 2014, the specifications of each of which are herebyincorporated by reference in their entirety.

BACKGROUND OF THE INVENTION Field of the Art

The disclosure relates to the field of processing qualitative responsesfrom a plurality of user devices, and more particularly to the field ofcomputing and selecting a next thought object for a plurality ofqualitative responses received from a plurality of user devices.

Discussion of the State of the Art

In systems where input from a plurality of user devices is solicited,understanding an effective distribution mechanism of thought objects forsolicitation of responses by a plurality of user devices is challenging.

Typically, when results from a plurality of user participant devices areused to gather input on a subject, two different types of participantresponses are elicited: quantitative responses and qualitativeresponses. A quantitative response is a close-ended response, such as amultiple choice, numeric style, or yes/no response. A qualitativeresponse is an open-ended, comment style response, where the participanthas freedom to textualize associated ideas and is not constrained bypre-determined answers. Accordingly, eliciting qualitative responses mayhave substantial benefits over quantitative responses in thatqualitative responses can provide more detailed information onparticipant interests, consisting of areas of alignment, sentiment ortopics, to name a few.

However, there are well known limitations with handling, evaluating, anddistributing qualitative responses, as compared to quantitativeresponses. The problem of distributing qualitative responses generalizesto dealing with what a next best qualitative response to display to oneor more user devices would be, in order to receive a more completeresponse pattern by the one or more user devices. Specifically, there isno easy way to ensure that the one or more user devices received adiverse enough set of qualitative responses to have a complete, or nearcomplete view of the subject at hand.

Further according to the art, many online engagement services offermethods of displaying items and information based on previous selectionsand engagements in a themed fashion, that is, showing future informationwhat has been previously liked or engaged by a user. These systems donot take into consideration a display of a diverse set of information tousers.

Accordingly, a need in the art exists for a system and method forcomputing a distribution pattern of information whereby thought objectsare displayed with equal coverage while reducing bias and displayingthought objects representing a diverse range of thoughts to one or moreuser devices.

SUMMARY OF THE INVENTION

Accordingly, the inventor has conceived and reduced to practice, in apreferred embodiment of the invention, a system and method comprising anetwork-connected thought object distribution computer comprising aprocessor, a memory, and programming instructions, the programminginstructions, when executed by the processor, cause the processor toreceive, at a project controller, a question object, from a leaderdevice, comprising an arrangement of information, distributing thequestion object to a plurality of participant devices and receiving aplurality of thought objects (each comprising additional arrangements ofinformation) from the plurality of participant devices; computing adistribution strategy for the plurality of thought objects, andredistributing, by the project controller, the plurality of thoughtobjects to at least a portion of the participant devices; whereby aplurality of filtering and section algorithms are executed by receivingrequest from a user device to rate a thought object, filtering at leasta portion of thought objects by custom selection method (referring toFIG. 9). Once filtered, zero or more filtered thought objects arereturned. If no filtered thought objects are returned then the algorithmceases, a notice may be sent to participant devices indicating anabsence of thought objects. The returned filtered thought objects areoptionally further filtered by random selection. The filtered thoughtobjects are then further filtered by a least seen algorithm (referringto FIG. 10), and finally selected by a topic algorithm. Once thoughtobjects are filtered, selected, and distributed, the project controllerthen receives a plurality of priority values from at least a portion ofthe plurality of user participant devices, whereby the plurality ofpriority values is each associated to a thought object.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several embodiments of theinvention and, together with the description, serve to explain theprinciples of the invention according to the embodiments. It will beappreciated by one skilled in the art that the particular embodimentsillustrated in the drawings are merely exemplary and are not to beconsidered as limiting of the scope of the invention or the claimsherein in any way.

FIG. 1 is a block diagram illustrating an exemplary hardwarearchitecture of a computing device used in an embodiment of theinvention;

FIG. 2 is a block diagram illustrating an exemplary logical architecturefor a client device, according to an embodiment of the invention;

FIG. 3 is a block diagram showing an exemplary architectural arrangementof clients, servers, and external services, according to an embodimentof the invention;

FIG. 4 is another block diagram illustrating an exemplary hardwarearchitecture of a computing device used in various embodiments of theinvention;

FIG. 5 is a plurality of objects used for thought object filtering andselection, according to a preferred embodiment of the invention;

FIG. 6 is a block diagram illustrating an exemplary conceptualarchitecture of a thought object selection computer, according to apreferred embodiment of the invention;

FIG. 7 is a flow diagram illustrating a method for conducting a processto solicit thought objects and priority value responses from a pluralityof devices, according to a preferred embodiment of the invention;

FIG. 8 is a flow diagram illustrating a method for thought objectselection based on a plurality of filtering and selection methods,according to a preferred embodiment of the invention;

FIG. 9 is a flow diagram illustrating a method for filtering a pluralityof thought object based on a plurality of custom selection rules,according to a preferred embodiment of the invention;

FIG. 10 is a flow diagram illustrating a method for filtering aplurality of thought objects by computing one or more thought objectsthat were least displayed to an output device of a user device,according to a preferred embodiment of the invention;

FIG. 11 is a flow diagram illustrating a method for filtering aplurality of thoughts based on a topic calculation, according to apreferred embodiment of the invention;

FIG. 12 is a flow diagram illustrating a method for calculating a topicvector, according to a preferred embodiment of the invention.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a system and methodfor computing a selection of a one or more thought objects fordistribution to a plurality of user devices whereby the one or morethought objects provide equal coverage while reducing bias and comprisethought objects representing a diverse range of thoughts.

One or more different inventions may be described in the presentapplication. Further, for one or more of the inventions describedherein, numerous alternative embodiments may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the inventions contained herein or the claimspresented herein in any way. One or more of the inventions may be widelyapplicable to numerous embodiments, as may be readily apparent from thedisclosure. In general, embodiments are described in sufficient detailto enable those skilled in the art to practice one or more of theinventions, and it should be appreciated that other embodiments may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularinventions. Accordingly, one skilled in the art will recognize that oneor more of the inventions may be practiced with various modificationsand alterations. Particular features of one or more of the inventionsdescribed herein may be described with reference to one or moreparticular embodiments or figures that form a part of the presentdisclosure, and in which are shown, by way of illustration, specificembodiments of one or more of the inventions. It should be appreciated,however, that such features are not limited to usage in the one or moreparticular embodiments or figures with reference to which they aredescribed. The present disclosure is neither a literal description ofall embodiments of one or more of the inventions nor a listing offeatures of one or more of the inventions that must be present in allembodiments.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or morecommunication means or intermediaries, logical or physical.

A description of an embodiment with several components in communicationwith each other does not imply that all such components are required. Tothe contrary, a variety of optional components may be described toillustrate a wide variety of possible embodiments of one or more of theinventions and in order to more fully illustrate one or more aspects ofthe inventions. Similarly, although process steps, method steps,algorithms or the like may be described in a sequential order, suchprocesses, methods and algorithms may generally be configured to work inalternate orders, unless specifically stated to the contrary. In otherwords, any sequence or order of steps that may be described in thispatent application does not, in and of itself, indicate a requirementthat the steps be performed in that order. The steps of describedprocesses may be performed in any order practical. Further, some stepsmay be performed simultaneously despite being described or implied asoccurring non-simultaneously (e.g., because one step is described afterthe other step). Moreover, the illustration of a process by itsdepiction in a drawing does not imply that the illustrated process isexclusive of other variations and modifications thereto, does not implythat the illustrated process or any of its steps are necessary to one ormore of the invention(s), and does not imply that the illustratedprocess is preferred. Also, steps are generally described once perembodiment, but this does not mean they must occur once, or that theymay only occur once each time a process, method, or algorithm is carriedout or executed. Some steps may be omitted in some embodiments or someoccurrences, or some steps may be executed more than once in a givenembodiment or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other embodiments of oneor more of the inventions need not include the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular embodiments may include multiple iterationsof a technique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of embodiments of the present invention inwhich, for example, functions may be executed out of order from thatshown or discussed, including substantially concurrently or in reverseorder, depending on the functionality involved, as would be understoodby those having ordinary skill in the art.

Definitions

A priority value, as referred to herein, is a response received from auser device and may be a scale represented by one or more stars,representations, or numbers (such as a Likert scale or a zero-centeredscale, or the like). In some embodiments, a zero-priority value is avalue usable by the system. In some embodiments, the scale isnormalized, in other embodiments the scale is a standard scale that mayor may not include negative values. In other embodiments, a priorityvalue scale may be a plurality of graphical elements indicating aspectrum of dislike to like, interest or sentiment level, or the like).In some embodiments, graphical scales are converted to a numeric scalefor calculation purposes.

In some embodiments, assigned, as referred to herein, for example, withrespect to a participant object 575 assigning a priority value to athought object, may refer to priority values that may have been receivedby a device 620 and associated to a thought object 510, the device 620associated to the participant object 575.

Rating, as referred to herein, may be a priority value response receivedfrom a device 620 associated to a participant object 575. Ratings may bea numeric value on a scale indicating a range of possible responsesavailable to assign to a thought object 510.

Hardware Architecture

Generally, the techniques disclosed herein may be implemented oncomputing hardware or a combination of programming instructions andhardware to form a specially programmed computer. For example, they maybe implemented in an operating system kernel, in a separate userprocess, in a library package bound into network applications, on aspecially constructed machine, on an application-specific integratedcircuit (ASIC), or on a network interface card.

An implementation based on programming instructions and hardware maydescribe at least some of the embodiments disclosed herein and may beimplemented on a programmable network-resident machine (which should beunderstood to include intermittently connected network-aware machines)selectively activated or reconfigured by computer program instructionsstored in memory. Such network devices may have multiple networkinterfaces that may be configured or designed to utilize different typesof network communication protocols. A general architecture for some ofthese machines may be described herein in order to illustrate one ormore exemplary means by which a given unit of functionality may beimplemented. According to specific embodiments, at least some of thefeatures or functionalities of the various embodiments disclosed hereinmay be implemented on one or more general-purpose computers associatedwith one or more networks, such as for example an end-user computersystem, a client computer, a network server or other server system, amobile computing device (e.g., tablet computing device, mobile phone,smartphone, wearable device, laptop, or other appropriate computingdevice), a consumer electronic device, a music player, or any othersuitable electronic device, router, switch, or other suitable device, orany combination thereof. In at least some embodiments, at least some ofthe features or functionalities of the various embodiments disclosedherein may be implemented in one or more virtualized computingenvironments (e.g., network computing clouds, virtual machines hosted onone or more physical computing machines, or other appropriate virtualenvironments).

Referring now to FIG. 1, there is shown a block diagram depicting anexemplary computing device 100 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 100 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory. Computing device 100 may be adaptedto communicate with a plurality of other computing devices, such asclients or servers, over communications networks such as a wide areanetwork a metropolitan area network, a local area network, a wirelessnetwork, the Internet, or any other network, using known protocols forsuch communication, whether wireless or wired.

In one embodiment, computing device 100 includes one or more centralprocessing units (CPU) 102, one or more interfaces 110, and one or morebusses 106 (for example, a peripheral component interconnect (PCI) bus).When acting under the control of appropriate software or firmware, CPU102 (i.e. processor) may be responsible for implementing specificfunctions associated with the functions of a specifically configuredcomputing device or machine. For example, in at least one embodiment, acomputing device 100 may be configured or designed to function as aserver system utilizing CPU 102, local memory 101 and/or remote memory120, and interface(s) 110. In at least one embodiment, CPU 102 may becaused to perform one or more of the different types of functions and/oroperations under the control of software modules or components, whichfor example, may include an operating system and any appropriateapplications software, drivers, and the like.

CPU 102 may include one or more processors 103 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors. In some embodiments, processors 103 may includespecially designed hardware such as application-specific integratedcircuits (ASICs), electrically erasable programmable read-only memories(EEPROMs), field-programmable gate arrays (FPGAs), and so forth, forcontrolling operations of computing device 100. In a specificembodiment, a local memory 101 (such as non-volatile random-accessmemory (RAM) and/or read-only memory (ROM), including for example one ormore levels of cached memory) may also form part of CPU 102. However,there are many ways in which memory may be coupled to system 100. Memory101 may be used for a variety of purposes such as, for example, cachingand/or storing data, programming instructions, and the like. It shouldbe further appreciated that CPU 102 may be one of a variety ofsystem-on-a-chip (SOC) type hardware that may include additionalhardware such as memory or graphics processing chips, such as a QualcommSNAPDRAGON™ or Samsung EXYNOS™ CPU as are becoming increasingly commonin the art, such as for use in mobile devices or integrated devices.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one embodiment, interfaces 110 are provided as network interfacecards (NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 110 may forexample support other peripherals used with computing device 100. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radiofrequency (RF), BLUETOOTH™, near-field communications (e.g., usingnear-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fastEthernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) orexternal SATA (ESATA) interfaces, high-definition multimedia interface(HDMI), digital visual interface (DVI), analog or digital audiointerfaces, asynchronous transfer mode (ATM) interfaces, high-speedserial interface (HSSI) interfaces, Point of Sale (POS) interfaces,fiber data distributed interfaces (FDDIs), and the like. Generally, suchinterfaces 110 may include physical ports appropriate for communicationwith appropriate media. In some cases, they may also include anindependent processor (such as a dedicated audio or video processor, asis common in the art for high-fidelity A/V hardware interfaces) and, insome instances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 1 illustrates one specificarchitecture for a computing device 100 for implementing one or more ofthe inventions described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 103 may be used, and such processors 103may be present in a single device or distributed among any number ofdevices. In one embodiment, single processor 103 handles communicationsas well as routing computations, while in other embodiments a separatededicated communications processor may be provided. In variousembodiments, different types of features or functionalities may beimplemented in a system according to the invention that includes aclient device (such as a tablet device or smartphone running clientsoftware) and server systems (such as a server system described in moredetail below).

Regardless of network device configuration, the system of the presentinvention may employ one or more memories or memory modules (such as,for example, remote memory block 120 and local memory 101) configured tostore data, program instructions for the general-purpose networkoperations, or other information relating to the functionality of theembodiments described herein (or any combinations of the above). Programinstructions may control execution of or comprise an operating systemand/or one or more applications, for example. Memory 120 or memories101, 120 may also be configured to store data structures, configurationdata, encryption data, historical system operations information, or anyother specific or generic non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device embodiments may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine-readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a Java™ compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Perl, Ruby, Groovy, or any other scripting language).

In some embodiments, systems according to the present invention may beimplemented on a standalone computing system. Referring now to FIG. 2,there is shown a block diagram depicting a typical exemplaryarchitecture of one or more embodiments or components thereof on astandalone computing system. Computing device 200 includes processors210 that may run software that carry out one or more functions orapplications of embodiments of the invention, such as for example aclient application 230. Processors 210 may carry out computinginstructions under control of an operating system 220 such as, forexample, a version of Microsoft's WINDOWS™ operating system, Apple's MacOS/X or iOS operating systems, some variety of the Linux operatingsystem, Google's ANDROID™ operating system, or the like. In many cases,one or more shared services 225 may be operable in system 200 and may beuseful for providing common services to client applications 230.Services 225 may for example be WINDOWS™ services, user-space commonservices in a Linux environment, or any other type of common servicearchitecture used with operating system 210. Input devices 270 may be ofany type suitable for receiving user input, including for example akeyboard, touchscreen, microphone (for example, for voice input), mouse,touchpad, trackball, or any combination thereof. Output devices 260 maybe of any type suitable for providing output to one or more users,whether remote or local to system 200, and may include for example oneor more screens for visual output, speakers, printers, or anycombination thereof. Memory 240 may be random-access memory having anystructure and architecture known in the art, for use by processors 210,for example to run software. Storage devices 250 may be any magnetic,optical, mechanical, memristor, or electrical storage device for storageof data in digital form (such as those described above, referring toFIG. 1). Examples of storage devices 250 include flash memory, magnetichard drive, CD-ROM, and/or the like.

In some embodiments, systems of the present invention may be implementedon a distributed computing network, such as one having any number ofclients and/or servers. Referring now to FIG. 3, there is shown a blockdiagram depicting an exemplary architecture 300 for implementing atleast a portion of a system according to an embodiment of the inventionon a distributed computing network. According to the embodiment, anynumber of clients 330 may be provided. Each client 330 may run softwarefor implementing client-side portions of the present invention; clientsmay comprise a system 200 such as that illustrated in FIG. 2. Inaddition, any number of servers 320 may be provided for handlingrequests received from one or more clients 330. Clients 330 and servers320 may communicate with one another via one or more electronic networks310, which may be in various embodiments any of the Internet, a widearea network, a mobile telephony network (such as CDMA or GSM cellularnetworks), a wireless network (such as WiFi, WiMAX, LTE, and so forth),or a local area network (or indeed any network topology known in theart; the invention does not prefer any one network topology over anyother). Networks 310 may be implemented using any known networkprotocols, including for example wired and/or wireless protocols.

In addition, in some embodiments, servers 320 may call external services370 when needed to obtain additional information, or to refer toadditional data concerning a particular interaction. Communications withexternal services 370 may take place, for example, via one or morenetworks 310. In various embodiments, external services 370 may compriseweb-enabled services or functionality related to or installed on thehardware device itself. For example, in an embodiment where clientapplications 230 are implemented on a smartphone or other electronicdevice, client applications 230 may obtain information stored in aserver system 320 in the cloud or on an external service 370 deployed onone or more of a particular enterprise's or user's premises.

In some embodiments of the invention, clients 330 or servers 320 (orboth) may make use of one or more specialized services or appliancesthat may be deployed locally or remotely across one or more networks310. For example, one or more databases 340 may be used or referred toby one or more embodiments of the invention. It should be understood byone having ordinary skill in the art that databases 340 may be arrangedin a wide variety of architectures and using a wide variety of dataaccess and manipulation means. For example, in various embodiments oneor more databases 340 may comprise a relational database system using astructured query language (SQL), while others may comprise analternative data storage technology such as those referred to in the artas “NoSQL” (for example, Hadoop Cassandra, Google BigTable, and soforth). In some embodiments, variant database architectures such ascolumn-oriented databases, in-memory databases, clustered databases,distributed databases, or even flat file data repositories may be usedaccording to the invention. It will be appreciated by one havingordinary skill in the art that any combination of known or futuredatabase technologies may be used as appropriate, unless a specificdatabase technology or a specific arrangement of components is specifiedfor a particular embodiment herein. Moreover, it should be appreciatedthat the term “database” as used herein may refer to a physical databasemachine, a cluster of machines acting as a single database system, or alogical database within an overall database management system. Unless aspecific meaning is specified for a given use of the term “database”, itshould be construed to mean any of these senses of the word, all ofwhich are understood as a plain meaning of the term “database” by thosehaving ordinary skill in the art.

Similarly, most embodiments of the invention may make use of one or moresecurity systems 360 and configuration systems 350. Security andconfiguration management are common information technology (IT) and webfunctions, and some amount of each are generally associated with any ITor web systems. It should be understood by one having ordinary skill inthe art that any configuration or security subsystems known in the artnow or in the future may be used in conjunction with embodiments of theinvention without limitation, unless a specific security 360 orconfiguration system 350 or approach is specifically required by thedescription of any specific embodiment.

FIG. 4 shows an exemplary overview of a computer system 400 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computer system 400 withoutdeparting from the broader spirit and scope of the system and methoddisclosed herein. CPU 401 is connected to bus 402, to which bus is alsoconnected memory 403, nonvolatile memory 404, display 407, I/O unit 408,and network interface card (NIC) 413. I/O unit 408 may, typically, beconnected to keyboard 409, pointing device 410, hard disk 412, andreal-time clock 411. NIC 413 connects to network 310, which may be theInternet or a local network, which local network may or may not haveconnections to the Internet. Also shown as part of system 400 is powersupply unit 405 connected, in this example, to ac supply 406. Not shownare batteries that could be present, and many other devices andmodifications that are well known but are not applicable to the specificnovel functions of the current system and method disclosed herein. Itshould be appreciated that some or all components illustrated may becombined, such as in various integrated applications (for example,Qualcomm or Samsung SOC-based devices), or whenever it may beappropriate to combine multiple capabilities or functions into a singlehardware device (for instance, in mobile devices such as smartphones,video game consoles, in-vehicle computer systems such as navigation ormultimedia systems in automobiles, or other integrated hardwaredevices).

In various embodiments, functionality for implementing systems ormethods of the present invention may be distributed among any number ofclient and/or server components. For example, various software modulesmay be implemented for performing various functions in connection withthe present invention, and such modules may be variously implemented torun on server and/or client components.

Conceptual Architecture

FIG. 5 is a plurality of objects used for thought object filtering andselection, according to a preferred embodiment of the invention.According to the embodiment, a plurality of programming instructionsstored in memory 240 that when executed by at least one processor 210comprise a plurality of objects that may comprise data, in the form offields, often known as attributes and programming instructions, in theform of procedures, often known as methods. Objects 500 may be arrangedsuch that procedures can access and often modify one or more data fieldsof an associated object. In various embodiments, programminginstructions enable objects to interact with one another. In a preferredembodiment, objects 500 may be implemented in an object-relationaldatabase management system, for example PostgreSQL.

Accordingly, It can be appreciated that an understanding of a pluralityof priority values received from a plurality of participant devices 620provides a means for large scale involvement of users via devices 620 ina networked environment to participate in a quantitative fashion toevaluate thoughts that require an understanding of interest regardlessof device location, temporal displacement (i.e. when the respondentsresponded), psychology (willingness to provide responses in an openforum, or requirement for anonymity), and the like. An interestcategorization may represent a collective understanding of what may bemost important to at least a portion of a group of users associated todevices 620, for example, across dispersed groups such thatunderstanding of concepts and perspective using accurate priority-basedindicators from a plurality of participant devices 620 by a plurality ofusers.

Tenant object 590 may be a plurality of programming instructions storedin memory 240 that when executed by one or more processors 210 describea tenant of system 600, that is, a configured entity that may execute aplurality of projects described by one or more associated projectobjects 539 for analysis of interest for a plurality of priority valuesreceived from a plurality of participant devices 620 associated to oneor more thought objects 510. Accordingly, one or more project objects539, that are associated to the tenant object 590, are connected byproject object pointer 594. In a preferred embodiment, tenant object 590may comprise: an object identifier 591 whereby each instantiation oftenant object 590 may be referred to uniquely within the system; tenantname 592 may be a text description of the instant tenant object 590;project object pointer 594 (described above) comprises one or morepointers to one or more project objects 539. Said differently, system600 may configure multiple tenant objects 590 whereby each tenant object590 may be associated to a plurality of project objects 539 whereby eachassociated project object 539 is associated to a plurality of otherobjects 500 (depicted in FIG. 5) to enable analysis of qualitativepatterns for a plurality of priority values received from a plurality ofparticipant devices 620. It should be appreciated that participantdevices 620 may be at least a portion of devices 620. In a preferredembodiment, participant devices 620 may be devices that, through network310, provided responses to, for example, a question object 546 and/orthought objects 510. In some embodiments, leader devices 622 (referringto FIG. 6) may be considered participant devices 620.

Project object 539 may be a plurality of programming instructions storedin memory 240 that when executed by processors 210 describe a projectfor an analysis of qualitative patterns for a plurality of priorityvalues received from a plurality of participant devices 620 based on oneor more thought objects 510 whereby a project may be a plannedcollaborative executions of the methods described herein utilizing oneor more specially programmed components 600 (referring to FIG. 6).Project object 539 may comprise: object identifier 540 which may be aglobally unambiguous persistent identifier representing an instance ofproject object 539; project name 541 may be textual description of theinstance of the project object 539; project code 545 may be uniqueidentifier associated to a project object 539. Thought object 510 may bea plurality of programming instructions stored in memory 240 that whenexecuted by processors 210 comprise an arrangement of information in theform of ideas received from a device 620 whereby an analysis ofqualitative patterns for a plurality of priority values received inresponse to the arrangement of information from a plurality ofparticipant devices 620. Thought object 510 may comprise: objectidentifier 511 which may be a globally unambiguous persistent identifierrepresenting an instance of thought object 510; thought_summary 513 maybe an arrangement of information corresponding to a qualitative responsefrom a device 620 to another arrangement of information in the form ofan open-ended question from, for example, a question object 546;thought_detail 514 may be an additional arrangement of informationcorresponding to an additional qualitative response from a device 620,for example, an explanation of the importance of the qualitativeresponse represented by thought_summary 513; shared_by 519 is a pointerto a participant object 575 who shared the instant thought object 510;process object pointer 536 may be a pointer to an associated processobject 569; question 537 may be a pointer to an associated questionobject 546 to, for example, have access to the question object 546through its memory address instead of a new object being created on astack.

Question object 546 may be a plurality of programming instructionsstored in memory 240 that when executed by processors 210 comprisedetails around the associated arrangement of information associated to acorresponding an open-ended question by, for example, as configured by aleader device 622, the arrangement of information being a point oforigination for which a plurality of thought objects 510 result, aredistributed by project controller 602, and for which a plurality ofpriority value responses are solicited from at least a portion ofdevices 620 to perform an analysis of qualitative patterns. Questionobject 546 may comprise, at least: object identifier 547 which may be aglobally unambiguous persistent identifier representing an instance ofquestion object 546; question text 548 may be an arrangement ofinformation comprising textual description in the form of an open-endedquestion; number 549 may be an additional unique identifier for theinstant question object 546 that may indicate an index of the instantquestion in a sequence or series of related question objects in aproject object 539; process object pointer 550 may be a pointer to anassociated process object 569, for example, to have access to theprocess object 569 through its memory address instead of a new objectbeing created on a stack.

Priority value object 559 may be a plurality of programming instructionsstored in memory 240 that when executed by processors 210 comprise anobject describing details around a priority value received from aparticipant device 620. It should be noted that in a typical analysis, aplurality of priority value objects may be associated to a thoughtobject 510 for an analysis of qualitative patterns as describedthroughout this specification. Priority value object 559 may comprise:object identifier 560 which may be a globally unambiguous persistentidentifier representing an instance of priority value object 559;thought object 562 may be a pointer to a corresponding thought object510, for example, to have access to the an associated thought object 510through its memory address instead of a new object being created on astack; participant 563 is a pointer to Participant object 575 thatassigned priority value 565 (mentioned below); priority value 565 may bea numeric identifier of the priority value received from a correspondingparticipant device 620 associated to the participant object 575referenced in participant pointer 563 (in some embodiments, priorityvalue 565 may be an alphanumeric value, a Boolean, an identifier to anemoticon or some other graphical representation, or the like); processobject 566 may be a pointer to a process object 569 to, for example,have access to the process object 569 through its memory address insteadof a new object being created on a stack.

Process object 569 may be a plurality of programming instructions storedin memory 240 that when executed by processors 210 comprise an objectdescribing a process corresponding to a project object 539 for ananalysis of qualitative patterns. A process may provide a procedure forhow a project is to be executed, for example, how thought objects 510will be distributed, how responses are received and processed, and thelike. Process object 569 may comprise: object identifier 570 which maybe a globally unambiguous persistent identifier representing an instanceof process object 569; name 571 may be textual description of theinstance of the process object 569; number 572 may be an additionalunique identifier associated to the instant process object 569; projectobject 574 may be a pointer to a corresponding project object 539 to,for example, have access to the project object 539 through its memoryaddress instead of a new object being created on a stack.

Participant object 575 may be a plurality of programming instructionsstored in memory 240 that when executed by processors 210 comprises anobject to describe a participant associated to a participant device 620(that is, each participant object corresponds to a corresponding device620). In some embodiments, participant objects may be assigned todevices 620 that have participated (for example, provided priorityvalues to one or more thought objects, or provided one or more thoughtobjects 510 in response to a question object 546). Participant object575 may comprise, at least: object identifier 576 which may be aglobally unambiguous persistent identifier representing an instance ofparticipant object 575; process object 579 may be a pointer to anassociated process object 569 to, for example, have access to theprocess object 569 through its memory address instead of a new objectbeing created on a stack; project object 580 may be a pointer to aproject object 539 to, for example, have access to the project object539 through its memory address instead of a new object being created ona stack; device ID identifies an associated user device 620.

It should be noted that, in a preferred embodiment, a tenant object 590may represent properties and methods corresponding to a user, or groupof users, of the system (for example, a company, organization, or thelike). Each tenant object 590 may be associated to one or more projectobject 539 that may provide details around a project for exchanginginformation following one or more processes associated to one or moreprocess objects 569 whereby at least one question object 546 and aplurality of thought objects 510 describe an interaction by devices 620(at least a portion of which are associated to a participant objects575) whereby interaction comprises, at least, an assignment of priorityvalues 559 to thought objects 510.

FIG. 6 is a block diagram illustrating an exemplary conceptualarchitecture of a thought object selection computer, according to apreferred embodiment of the invention. According to the embodiment,thought object selection environment 600 comprises a plurality ofcomponents each comprising at least a plurality of programminginstructions, the programming instructions stored in memory 240 thatwhen executed by one or more processors 210, cause one or more processor210 to perform operations disclosed herein. In a preferred embodiment, aquestion object 546 is received at a project controller 709 from a firstleader device 622. The question object 546 may then be distributed, byproject controller 602 to at least a portion of a plurality of devices620, subsequently, a plurality of thought objects 510 may be receivedfrom at least a portion of the at least portion of the plurality ofdevices 620 whereby the thought objects 510 may be redistributed to atleast a portion of the plurality of devices 620 with a request for anassignment, by at least a portion of the plurality of user participantdevices 620, of one or more priority values. It should be appreciatedthat questions objects 546 when received from a leader device 622, theobjects and associated parameters may be stored in object database 611.Similarly, thought objects 510 received from the at least portion of theat least portion of the plurality of devices 620 may be stored in objectdatabase 611. In some embodiments, one or more priority value objects559 comprising the one or more priority values associated to acorresponding thought object 510 of the plurality of thought objects 510are received by one or more devices 620. In some embodiments, projectcontroller 602 processes methods herein based at least in part onconfiguration within project object 539 and process object 569.

Prior to project controller 602 processing the plurality of priorityvalue objects 559 for the plurality of thought objects 510 from theplurality of devices 620, object selector 607 may use algorithmsdescribed herein (referring to FIGS. 8-12) to filter and select objectsfor distribution to one or more devices 620. It should be appreciatedthat question object 546 and at least a portion of the plurality ofthought objects (and associated other objects 500) are associated to atleast one project object 539. In a preferred embodiment, a tenant object590 may have one or more associated projects 539, that is, that a tenantmay perform a plurality of mutually exclusive projects (also referred toherein as an exchange) to understand the dynamics and behaviors of aplurality of users via a plurality of devices. Though in a preferredembodiment, projects are self-contained in nature (in terms of analysesthat are performed), it should be appreciated that in some embodiments,projects may be interrelated, and calculations by system 600, may beperformed across a plurality of projects.

According to some embodiments, each thought object 510 must meet certaincriteria in order to qualify for inclusion into a filter and selectcomputation. These criteria are combinations of meeting (or failing tomeet) certain thresholds, as analyzed by thought-text parser 609, topiccalculator 608 and object selector 607.

Device interface 601 may manage input/output communications to devices620, and in some embodiments, to response database 630, over network310.

Project controller 602 manages an execution of a thought object exchangeproject whereby project controller 602 may manage receiving anddistributing question objects 546 to devices 620, manage receiving anddistributing thought objects 510, and receiving and distributingpriority value objects 559 via device interface 601.

In a preferred embodiment, object selector 607 filters and selects oneor more thought objects 510 for distribution to one or more devices 620.Techniques for filtering and selection by objects selector 607 mayinclude, but not limited to, diversity of thought object 510 based on ananalysis of thought object 510 topics, frequency of delivery of athought object 510 to one or more devices 620, random selection, and thelike.

In a preferred embodiment topic calculator 608 may be used by methodsdisclosed herein to compute a topic of which text within one or morethought objects 510 may represent, that is, quantitatively determine acomputed difference between a plurality of thought objects 510 based oninformational contents within one or more thought object 510. Further tothe embodiment, topic calculator 608 may calculate topic vectors andtopic tables using methods described herein.

In a preferred embodiment, thought-text parser 609 may generate aplurality of text based on a vocabulary, the vocabulary generated byparsing text from one or more thought objects 510 (for example, fromthought_summary 513 and/or thought_detail 514). In some embodiments textoriginated from an automatic speech recognition process as is known inthe art (not shown). In some embodiments, thought-text parser 609, maymodify word contents of the plurality of text by, for example, removingstop words, stemming words, tokenizing words, determine frequency ofwords, etc. Topic generator 613 may generate a plurality of topicvectors in a topic table, each topic vector associated to a thoughtobject 510 of the plurality of thought objects 510. A topic vector isused to identify topics associated with a thought calculated based ontechniques described herein (referring to FIG. 12). Topic vectors may beused to calculate a diversity score whereby diversity is defined as aEuclidean distance between thought objects previously rated by aparticular participant device 621 and thought objects not yet rated bythe particular participant device 621 (that is, one or more thoughtobjects 510 available to be selected for distribution to the particularparticipant device 621).

Response database 610 may store received response information from theplurality of devices 620. In some embodiments, response database 610holds just priority value responses while in others, priority valueresponses are held in priority value objects 559. Object database 611may provide database storage for objects 500 and 600 both pre-configuredand objects with assigned data fields. Configuration database 612provides storage for systems configuration components, for example, atleast, configuration for devices 620, system components 600, and thelike. It can be appreciated by one with ordinary skill in the art thatthe above referenced databases provide an exemplary set of databasesrequired to implement system 600 components and data necessary toexecute the disclosed methods.

Devices 620 comprise participant devices 621 and leader devices 622. Aleader device 622 may configure a project object 539 associated to oneor more question objects 546 to solicit a plurality of thought objects510 based on an arrangement of information in the form of an open-endedfree-flow question for the purpose of receiving priority value responsesreceived, by project controller 602, from at least a portion ofplurality of participant devices 620 (whereby the at least portion ofdevices may be hereinafter referred to as participant devices 620) andstored in a plurality of priority value objects 559 for analysis bysystem 600. In a preferred embodiment, leader devices 622 may initiateand manage a project (as defined in a project object 539 that comprisesone or more question objects 546 via a process defined in process object569) and at least a portion of participant devices 621 (i.e. those thathave responded, comprise participant objects 620. In some embodiments,leader devices 622 may be considered participant devices and may act asboth a leader device 622 and a participant device 621.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

FIG. 7 is a flow diagram illustrating a method for conducting a processto solicit thought objects and priority value responses from a pluralityof devices, according to a preferred embodiment of the invention.According to the embodiment, in a first step 701, a plurality ofconnections from a plurality of devices 620 are received via network 310at device interface 708 to enable communication between system 600 andconnected devices 620 and, in some embodiment, remote response database630. In a next step 702, question object 546 is received, by projectcontroller 602, from a first leader device 622 via device interface 601to begin a process to solicit thought objects and priority valueresponses. It should be appreciated that question object 546 may beassociated to a previously configured project object 539 and belong to atenant object 590. Question object 546 may comprise an arrangement ofinformation comprising open-ended free-form text arranged in a mannerwhereby responses from at least a portion of participant devices 621 maybe solicited (for example, arranged in the form of a question), theexpected responses comprising a plurality of thought objects 510. In anext step 703, project controller 602 may then distribute questionobject 546 to at least a portion of devices 620 via device interface 610on network 310. In a next step 704, a plurality of thought objects 510may be received by at least a portion of devices 620, the plurality ofthought objects 510 each comprising, at least, an arrangementinformation (for example, within thought_detail 514), the arrangement ofinformation comprising open-ended free-form text arranged in a mannerwhereby responses from at least a portion of participant devices 621 maybe solicited, the expected responses comprising a plurality of priorityvalue objects 559, each priority value object 559 comprising priorityvalue 565 comprising a priority value associated to a thought object(for example thought object 562 may associate priority value 565 to acorresponding thought object 510). Further in step 704, topic generator613 generates a plurality of topic vectors in a topic table, each topicvector associated to a thought object 510 of the plurality of thoughtobjects 510 (referring to FIG. 12) In a next step 705, the plurality ofthought objects 510 may be distributed, by project controller 602, to atleast a portion of user devices 620 via device interface 610 overnetwork 310, utilizing thought selection algorithms described herein(referring to FIG. 8) to one or more devices 620. In a next step 706,project controller 602 may receive a plurality of priority value objects559 (herein referred to as priority value responses) from at least aportion of devices 620, the plurality of priority value responses eachassociated to a corresponding thought object 510 (as describedpreviously), the at least portion of responding devices 620 hereinreferred to as participant devices 620, each priority value responseassociated to a corresponding thought object 510 and a correspondingparticipant device of the participant devices 620. In a next step 707,project controller 602 may store the plurality of thought objects 510and associated priority value responses in response database 610 (insome embodiments, project controller 602 may store the plurality ofthought objects 510 and associated priority value responses in remoteresponse database 630 via network 310).

FIG. 8 is a flow diagram illustrating a method for thought objectselection based on a plurality of filtering and selection methods,according to a preferred embodiment of the invention. According to theembodiment, method 800 starts in a first step 801, where a request isreceived, from a first participant device 621, to rate a thought object510. In a next step 802, project controller 602 checks a quantity ofshared thought objects 510. In a next step 803, all thought objects 510are retrieved. In a next step 804, at least a portion of thought objects510 are filtered, by custom selection method (referring to FIG. 9). Oncefiltered, zero or more filtered thought objects 510 (herein referred toas filtered thought objects 510 N1) are returned. If no filtered thoughtobjects 510 are returned (that is, N1=0), in step 805, then no thoughtobjects 510 are available for selection. Otherwise, in a next step 806,if more than one thought object 510 are returned (that is N1>1), thenthe returned filtered thought objects 510 (from step 804), are furtherfiltered, in a next step 808, by random selection (herein referred to asN2 which are at least a portion of filtered thought objects 510. In apreferred embodiment five filtered thought objects 510 may be selectedat random from N1. In other embodiments, any pre-configured number offiltered thought objects 510 may be selected at random from N1. In anext step 809, filtered thought objects 510 N2 are further filtered byleast seen (referring to FIG. 10), that is, a selection of one or morethought objects 510 by those that were delivered to participant devices621 the least amount of times as compared to other thought objects 510,the returned filtered thought objects 510 herein referred to N3representing at least a portion of thought objects 510 from N1 or N2. Ina next step 810, if N3 filtered thought objects 510 is greater than one,then a thought object 510 is selected by topic (i.e. most diverse) instep 811 (referring to FIG. 11). In a next step 812, final filteredthought 510 is shown to a first user device 620.

FIG. 9 is a flow diagram illustrating a method for filtering a pluralityof thought object based on a plurality of custom selection rules,according to a preferred embodiment of the invention. According to theembodiment, a plurality of programming instructions stored in memory 240when executed by one or more processors 210 cause the one or moreprocessors 210 to perform method 900 for custom selection of one or morefiltered thought objects 510 begins in a first step 901 whereby thoughtobjects are received by object selector 607 (for example from objectdatabase 611). In a next step 902, the plurality of thought objects 510may be selected, in a first filtering step (1), by object selector 607,whereby the selected filtered thought objects 510 are at least a portionof thought objects 510 not previously shown (that is delivered) to anoutput device 260 associated to a first participant device 621. In anext step 910, if no thought objects 510 remain, then a notificationthat no thought objects 510 are available is returned to the participantdevice 621, in step 911. Otherwise if there are thought objects 510remaining from filtering step (1), the process continues. In a next step903, if the first participant device 621 selects a thought object 510 ata predefined interval (In a preferred embodiment, the predefinedinterval may be, the 5^(th), 10^(th), 15^(th), etc. thought object 510,i.e. an interval divisible by 5; however, any interval or pattern may beused), then object selector 607 considers only thought objects 510, in asecond filtering step (2), shared by the first participant device 621,that is, thought objects 510 associated to the first participant device621. In a next step 904, thought selector may consider only thoughtobjects 510, in a third filtering step (3), that are not associated toanother participant device 621, to which another thought object 510,selected by the thought selector in response to a previous request fromfirst participant device 621, is associated, that is, thought objects510 not shared by the same user as a thought object 510 previouslyselected by object selector 607 for the first participant device 621. Ina next step 905, if there are one or more filtered thought objects 510,then the filtered thought objects 510 are returned by object selector607 in step 909; otherwise, if no filtered thought objects 510 result,then the third filtering step is ignored and only filtered thoughtobjects 510, filtered by a first filtering step (1) and a secondfiltering step (2) are considered in step 907. Further in step 907, ifno thought objects 510 result from a first filtering step (1) and secondfiltering step (2), then the second filtering step (2) is ignored, byobject selector 607, and filtered thought objects 510 from a firstfiltering step (1) only are returned, in step 909, by object selector607.

FIG. 10 is a flow diagram illustrating a method for filtering aplurality of thought objects by computing one or more thought objectsthat were least displayed to an output module of a participant device,according to a preferred embodiment of the invention. According to theembodiment, a plurality of programming instructions stored in memory 240and when executed by at least one processor 210 cause the at least oneprocessor 210 to perform a method for filtering and selecting one ormore thought objects 510 that were least displayed on an output device260 of an associated participant device 621 starting with a first step1001, object selector 607 initializes a tolerance (T), for example to azero value. In a next step 1002, a rating table, from object database611, may be queried to determine a count for a number of rows associatedto each thought object 510, representing the number of times the thoughtobject 510 has been displayed on an output device 260 of an associatedparticipant device 621. In a next step 1003, Identify, by objectselector 607, the lowest row count (min_row_count) for all thoughtobjects 510. In a next plurality of steps 1004, object selector 607identifies one or more thought objects 510 by selecting thought objectsassociated to ratings tables with a row count that may be less than orequal to the lowest row count (referring to step 1003) plus a toleranceby: iteratively analyzing each rating table associated to each thoughtobject 510, in step 1005, whereby if a row count is greater than themin_row_count plus the tolerance, the thought object 510 is removed fromconsideration in step 1006. Once all thought objects have been analyzed,the remaining thought objects 510 are returned in step 1007.

FIG. 11 is a flow diagram illustrating a method for filtering aplurality of thoughts based on a topic calculation, according to apreferred embodiment of the invention. According to the embodiment, aplurality of programming instructions stored in memory 240 and whenexecuted by at least one processor 210 cause the at least one processor210 to perform a method for filtering a plurality of thoughts based on atopic calculation beginning in a first step 1101, object selector 607retrieves the most recent M thought objects 510 previously distributedto the associated participant device 621, where M is 1 in a preferredembodiment (however, in some embodiments, any number may be used for M).That is, M thought objects 510 representing thoughts previouslydisplayed to the associated participant device 621.

In a plurality of steps 1102, for each M thought object 510, objectselector 607 may perform the following steps: In step 1103, each Mthought object 510 may be checked to see if it has a row in a topictable (as previously calculated referring to FIG. 12) wherein the topictable comprising a plurality of vectors that numerically representtopics associated to thought objects 510. If the M thought object 510does not have a row in the topic table, in a next step 1104, the thoughtobject 510 is ignored, that is the thought object 510 is not consideredin a resulting the filtered set; otherwise, if there is an associatedrow in a topic table, the topic vector (i.e. the row) associated to thethought is retained, in step 1105, and the process continues at step1103 until all M thought objects 510 are analyzed.

Once all topic vectors are analyzed for all M thought objects 510 thenumber of remaining thoughts, from the plurality of steps 1102, isanalyzed in step 1106. If there are no remaining M thought objects 510then a thought object 510 is selected at random from N3 (referring toFIG. 8) 1107 to be distributed to the associated participant device 621;otherwise, a plurality of steps 1108 will be processed on the remainingN3 thought objects 510 in order to select the most diverse thought,relative to the previous M thoughts, to select for distribution to theassociated participant device 621.

In a plurality of steps 1108, For each N3 thought object, objectselector 607 may perform the following steps: in step 1109, a check todetermine if the thought object 510 has a row in the topic table isperformed. If it does not, in a next step 1110, the thought object 510is ignored; otherwise, if there is an associated row in a topic table, acount of thought objects 510 with topic vectors is calculated in step1111.

Once all topic vectors are analyzed for all N3 thought objects 510, thecount of thought objects with topic vectors, from the plurality of steps1108, is analyzed in step 1112. If the count is zero then a thoughtobject 510 is selected at random from N3 in step 1113; otherwise, steps1114 is initialized. In step 1114, a max score is initialized (forexample to zero) and a plurality of steps 1114 may be processed on theremaining N3 thought objects 510 beginning with step 1115, wherebyobject selector 607 determines if a first remaining thought object 510has an associated row in a topic table. If so, the vector (i.e. row nV)is retrieved in step 1116; otherwise object selector 607 creates, instep 1117, a default vector (nV) of an expected length (that is thelength of current vectors for other vectors; it should be noted that anexpected length would have been known from step 1112 since a check wasperformed for any N thought objects 510 having a topic vector), thevalues of the created vector summing to 1. In a next step 1118, for eachremaining M thought with a vector in the topic table (mV), a Euclideandistance between mV and nV is calculated, in step 1119, by topiccalculator 608. In a next step 1120, a product of all Euclideandistances is calculated, by topic calculator 608, to get a diversityscore. In a next step 1121, the diversity score is compared to themax_score, by topic calculator 608. If diversity score is less than themax_score, the method returns to step 1115 to process a next N thoughtobjects 510; otherwise the max_score is updated to equal the calculateddiversity score, in step 1122, and the method returns to step 1115 toprocess a next N thought objects 510.

Once all N3 thought objects 510 are processed, in a next step 1123, allthought objects from N thought objects 510 that each have diversityscore equal to max_score is selected by object selector 607. If only onethought object remains in step 1124, the one thought object 510 isreturned as the filtered thought 510; otherwise, in step 1125, a mostrecently shared thought object 510 is selected and returned as thefiltered thought object in step 1126.

FIG. 12 is a flow diagram illustrating a method for calculating a topicvector, according to a preferred embodiment of the invention. Accordingto the embodiment, a plurality of programming instructions stored inmemory 240 and executed by one or more processors 210 cause theprocessor to perform a method for calculating a topic vector whereby avocabulary may be created in a plurality of thought objects parsed bythought-text parser 609 (for example, based on thought_summary 513 andthought_detail 514 associated to a thought object 510) of all thoughtobjects 510 available in an exchange by machine learning and naturallanguage processes disclosed herein to create a statistical model todiscover “topics” that occur in one or more thought objects 510 by usingtext-mining techniques known in the art to discover structures in a textbody.

Accordingly, in a first step 1201, a shared thought object 510 isreceived, by the project controller 602, from a first participant device621 of a plurality of participant devices 621. In a next step 1202, atleast a quantity N of thought objects that need topic vectors calculatedis determined, by topic calculator 608, whereby N may be apre-configured or a dynamically calculated number. If no thought objectsneed a calculation of a topic vector, in a next step 1219, nocalculation is performed. If at least a quantity N of thought objectsneed topic vectors, in step 1203, all thought objects in an exchange areretrieved. In a next plurality of steps 1204, for each thought object510 the following steps are performed by topic calculator 608: In step1207, thought object 510 is converted to lower case. In a next step1208, a tokenizer library (for example, Natural Language Toolkit (NLTK))may be used to tokenize thought object 510, that is, assign a token toeach word. In a next step 1209, thought-text parser 609 may remove stopwords from thought object 510 whereby the stop words may bepre-configured in a list. Accordingly, the tokens associated to the stoplist words are removed, by thought-text parser 609, from thought object510. In a next step 1210, thought-text parser 609 may stem the remainingtokens (for example, using NLTK stemming library), by shortening wordsto their root value by a process of reducing inflected (or sometimesderived) words to their word stem, base or root form using a stemmingprogram, stemming algorithm, or other stemmer techniques known in theart.

In a next step 1211, an array of all stemmed-tokens from all thoughtobjects 510 may be created by thought-text parser 609. In a next step1212, a list of all unique stemmed-tokens may be created by thought-textparser 609. In a next plurality of steps 1213, for each thought object510 the following steps are performed by topic calculator 608: in step1214, an empty vector the length of all unique stemmed-tokens may becreated; in a next step 1215, a frequency of each stemmed-token inthought object 510 is computed and added to the vector.

In a next step 1216, the number of topics for a latent Dirichletallocation (LDA) model may be determined by document generator 609. Inan exemplary embodiment, a number of topics N may be found using anyinteger value based on the number of thought objects 510. For example, Nmay be: 10, if fewer than 21 thought objects 510; 20, if fewer than 31thought objects 510; 30, if fewer than 41 thought objects 510; 40, iffewer than 51 thought objects 510; 50, if more than 50 thought objects510. In a next step 1217, an LDA model is generated using stemmed-tokencounts for each thought object 510. In a next step 1218, a plurality oftopic vectors is retrieved, by thought-text parser 609, from the LDAmodel for each thought object 510 and saved to object database 611 as athought object topic table.

The skilled person will be aware of a range of possible modifications ofthe various embodiments described above. Accordingly, the presentinvention is defined by the claims and their equivalents.

What is claimed is:
 1. A system for selecting a thought object, from aplurality of thought objects, to send to a user device comprising: athought object selection computer comprising a memory, a processor, anda plurality of programming instructions, the plurality of programminginstructions when executed by the processor cause the processor to:receive a plurality of connections from a plurality of user devices overa network; receive a question object comprising, at least, anarrangement of information from a leader user device of the plurality ofuser devices; send the question object to at least a portion of theplurality of user devices; receive a plurality of thought objects fromat least a portion of user devices; determine a filtered set of thoughtobjects, of the plurality of thought objects, to distribute to the atleast portion of the plurality of user devices, the filtered set ofthought objects determined by custom selection; upon a quantity of thefiltered set of thought objects determined by custom selection beinggreater than a quantity of zero then: if the filtered set of thoughtobjects is a quantity of one then send the filtered set of thoughtobjects to a first user device; otherwise: if the quantity of thefiltered set of thought objects is greater than a pre-configured amountthen optionally update the filtered set of thought objects by randomlyselecting a pre-configured subset of the filtered set of thoughtobjects; further filter the filtered set of thought objects bydetermining one or more least seen thought objects; if only one thoughtobject remains of the filtered set of thought objects, then send thefiltered set of thought objects to the first user device;  otherwise: select a most diverse thought object of the filtered set of thoughtobjects and send the most diverse thought object to the first userdevice.
 2. The system of claim 1, wherein custom selection comprises afurther plurality of programming instructions, the further plurality ofprogramming instructions when executed by the processor cause theprocessor to: (a) compute if there is at least one thought object, ofthe plurality of thought objects, that has not already been shown to thefirst user device, if so, select the at least one thought objects; (b)compute if the first user device is requesting an nth thought object,the nth number being a multiple of a preconfigured nth number, thenselect at least one thought object shared by the first user device; (c)compute if there is at least one thought object that was not shared by auser device associated to a previously selected thought object assignedto the first user device, select the at least one thought object; (d) ifno thought objects have been selected after performing steps (a), (b),and (c), select the at least one thought objects remaining from step(b); (e) if no thought objects have been selected after performing steps(a) and (b), select the at least one thought objects remaining from step(a).
 3. The system of claim 1, wherein determining one or more leastseen thought objects comprises a further plurality of programminginstructions, the further plurality of programming instructions whenexecuted by the processor cause the processor to: set a pre-configuredtolerance; determine a plurality of row counts, each row associated toratings associated to a thought object of the plurality of thoughtobjects; identify a lowest row count of the plurality of row counts; foreach thought object of the plurality of thought objects: if anassociated row count is greater than a sum of the lowest row count plusthe tolerance, then removing the thought object from the filtered set ofthought objects.
 4. The system of claim 1, wherein a selection of themost diverse thought object comprises a further plurality of programminginstructions, the further plurality of programming instructions whenexecuted by the processor cause the processor to: compute a topic vectorfor each thought object, of the plurality of thought objects, comprisingthe steps of: process text associated to the thought object to make itsuitable for topic modeling; generate a token for each though object andcount the frequency of each token across the plurality of thoughtobjects; generate, using a latent Dirichlet allocation, a plurality oftopic vectors, each topic vector associated to a thought object of theplurality of thought objects.
 5. The system of claim 4, wherein to makethe text suitable for topic modeling comprises a further plurality ofprogramming instructions, the further plurality of programminginstructions when executed by the processor cause the processor to:convert thought object text to lower case; split the text into tokens;remove tokens that exist in a pre-configured referenced list of stopwords; and, stem the remaining tokens.
 6. The system of claim 1, whereina selection of the most diverse thought object further comprising afurther plurality of programming instructions, the further plurality ofprogramming instructions when executed by the processor cause theprocessor to: receive a plurality of previous topic vectors, eachprevious topic vector of the plurality of previous topic vectorsassociated to a thought object previously assigned to the first userdevice; receive a plurality of target topic vectors, each target topicvector associated to a thought object of the filtered set of thoughtobjects; calculate, for each target topic vector of the plurality oftopic vectors, a Euclidean distance between that target topic vector andat least a portion of previous topic vectors; compute for each filteredthought object, a diversity score by multiplying the Euclidean distancesof an associated topic vector to each previous topic vector of theplurality of previous topic vectors; select one or more diverse thoughtobjects of the filtered thought objects with a highest diversity score;if a quantity of the one or more diverse thought objects is one, thensend the one or more diverse thought object to the first user device;otherwise select the most recently shared thought object and send themost recently shared thought object to the first user device.
 7. Thesystem of claim 1, wherein the pre-configured amount is a quantity offive.
 8. The system of claim 1, wherein the pre-configured subset is aquantity of five.
 9. A computer-implemented method for selecting athought object, from a plurality of thought objects, to send to a userdevice comprising the steps of: receiving, at a device interface, aplurality of connections from a plurality of user devices over anetwork; receiving, at a project controller, a question objectcomprising, at least, an arrangement of information from a leader userdevice of the plurality of user devices; sending, by the projectcontroller, the question object to at least a portion of the pluralityof user devices; receiving, at the project controller, a plurality ofthought objects from at least a portion of user devices; determining afiltered set of thought objects of the plurality of thought objects todistribute to the at least portion of user devices, the filtered set ofthought objects determined by custom selection; upon a quantity of thefiltered set of thought objects determined by custom selection beinggreater than zero then: if the filtered set of thought objects is aquantity of one then select the thought object and send the filtered setof thought objects to a first user device; otherwise: if the quantity ofthe filtered set of thought objects is greater than a pre-configuredamount then optionally update the filtered set of thought objects byrandomly selecting a pre-configured subset of the filtered set ofthought objects; further filter the filtered set of thought objects bydetermining one or more least seen thought objects; if only one thoughtobject remains of the filtered set of thought objects, then send thefiltered set of thought objects to the user device; otherwise: select amost diverse thought object of the filtered set of thought objects andsend the most diverse thought object to the first user device.
 10. Themethod of claim 9, wherein custom selection comprises the steps of: (a)if there is at least one thought object, of the plurality of thoughtobjects, that has not already been shown to the first user device,selecting the at least one thought objects; (b) if the first user deviceis requesting an nth thought object, the nth number being a multiple ofa preconfigured number, then selecting at least one thought objectshared by the first user device; (c) if there is at least one thoughtobject that was not shared by a user device associated to a previouslyselected thought object assigned to the first user device, selecting theat least one thought object; (d) if no thought objects have beenselected after performing steps (a), (b), and (c), then, select the atleast one thought objects remaining from step (b); (e) if no thoughtobjects have been selected after performing steps (a) and (b) then,select the at least one thought objects remaining from step (a).
 11. Themethod of claim 9, wherein determining one or more least seen thoughtobjects comprises the steps of: setting a pre-configured tolerance;determining, at a ratings table in a database, a plurality of rowcounts, each row associated to ratings associated to each thought objectof the plurality of thought objects; identify a lowest row count of theplurality of row counts; for each thought object of the plurality ofthought objects: if an associated row count is greater than a sum of thelowest row count plus the tolerance, then removing the thought objectfrom the filtered set of thought objects.
 12. The method of claim 9,wherein the most diverse thought object is selected by computing a topicvector for each thought object, of the plurality of thought objects,comprising the steps of: processing text associated to the thoughtobject to make it suitable for topic modeling; generating a token foreach though object; counting the frequency of each token across theplurality of thought objects; generating, using a latent Dirichletallocation, a plurality of topic vectors, each topic vector associatedto a thought object of the plurality of thought objects.
 13. The methodof claim 12, wherein making the text suitable for topic modelingcomprises the steps of: converting thought object text to lower case;splitting the text into tokens; removing tokens that exist in apre-configured referenced list of stop words; and, stemming theremaining tokens.
 14. The method of claim 9, further comprising thesteps of: receiving, from a project controller, a plurality of previoustopic vectors, each previous topic vector of the plurality of previoustopic vectors associated to a thought object previously assigned to thefirst user device; receiving a plurality of target topic vectors, eachtarget topic vector associated to a thought object of the filtered setthought objects; for each target topic vector, of the plurality of topicvectors, calculating a Euclidean distance between that target topicvector and at least a portion of previous topic vectors; for eachfiltered thought object computing a diversity score by multiplying theEuclidean distances of an associated topic vector to each previous topicvector of the plurality of previous topic vectors; select one or morediverse thought objects of the filtered thought objects with a highestdiversity score; if a quantity of the one or more diverse thoughtobjects is one, then send the one or more diverse thought object to thefirst user device; otherwise select the most recently shared thoughtobject and send the most recently shared thought object to the firstuser device.
 15. The method of claim 9, wherein the pre-configuredamount is a quantity of five.
 16. The method of claim 9, wherein thepre-configured subset is a quantity of five.